Abstract
This paper introduces an improved DV-hop-based localization technique to solve the link failure problem without using extra hardware during the continuous localization process because of the uncertainty of the underwater channel, limited energy resources, and technical glitches. On the other hand, low accuracy and poor stability are the main problems of the traditional DV-hop technique. This paper also introduces a hybrid TDoA-DV hop algorithm to overcome these problems and evaluate the position of the target nodes. In this algorithm, the average hop distance is replaced with improved hop distance to suppress the hop distance error, and the Wild Horse Optimization (WHO) algorithm is used for position estimation to find out the best and fast optimum location of the target node and achieve better accuracy and stability. The experimental result of the proposed work is compared to existing works, including the traditional DV-hop, improved DV-hop, and improved shortest path +PSO. The simulation result shows that the proposed method improves the average accuracy by 67.9%, 56.24%, and 3.6%, and stability by 61.9%, 45.9%, and 5.15% approximately. The recommended technique is useful for localization in any static ad-hoc wireless or underwater sensor network due to its better accuracy and stability.
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Abbreviations
- \(\tau_{n}\) :
-
Transit time difference between the source and microphone
- v:
-
Speed of sound
- \(d_{n}\) :
-
Distance between the source and n microphone
- \(D_{n}\) :
-
Distance between two different sensor nodes
- \(HopSize_{m}\) :
-
Average distance per hop
- \(hop\_count_{u,m}\) :
-
Hop count between anchor m and target node u
- p:
-
Population
- M:
-
Total population
- g:
-
Number of groups
- \(P_{s}\) :
-
Percentage of the stallion
- \(Y_{i,g}^{t}\) :
-
The current location of the group member
- \(St^{t}\) :
-
The location of a stallion (leader)
- \(\overline{Y}_{i,g}^{t}\) :
-
The new position of the group during grazing
- \(\vec{r}_{1} ,\vec{r}_{3}\) :
-
Random vector
- \(i_{t\max }\) :
-
Maximum iteration
- \( S\overline{t}_{gi} ,St_{gi}\) :
-
The next and current position of ith leader
- \(Hp^{eff}\) :
-
The adequate avg hop size of p anchor node
- \(Avg\_Hp_{p}^{error}\) :
-
The improvement factor
- \(I\) :
-
Identity matrix
- \(C_{r + 1}\) :
-
Scaling factor
- \(\phi_{r}\) :
-
Error factor
- β:
-
Large positive integer
- \(R_{p,q}^{TDoA}\) :
-
Distance using the TDoA
- \(P_{c}\) :
-
Crossover percentage
- \(P_{s}\) :
-
Stallions percentage
- f:
-
Fitness function
- \(W_{h}\) :
-
The position of the water hole
References
Adnan M, Razzaque M, Ahmed I, Isnin I (2013) Bio-mimic optimization strategies in wireless sensor networks: a survey. Sensors 14:299–345. https://doi.org/10.3390/s140100299
Allotta B, Bartolini F, Caiti A et al (2015) Typhoon at CommsNet13: experimental experience on AUV navigation and localization. Annu Rev Control 40:157–171. https://doi.org/10.1016/j.arcontrol.2015.09.010
Bulusu N, Heidemann J, Estrin D (2000) GPS-less low-cost outdoor localization for very small devices. IEEE Pers Commun 7:28–34. https://doi.org/10.1109/98.878533
Cao Y, Zhao N, Yu FR et al (2018) Optimization or alignment: secure primary transmission assisted by secondary networks. IEEE J Sel Areas Commun 36:905–917. https://doi.org/10.1109/JSAC.2018.2824360
Chen X, Zhang B (2012) Improved DV-hop node localization algorithm in wireless sensor networks. Int J Distrib Sens Networks 8:213980. https://doi.org/10.1155/2012/213980
Das AP, Thampi SM (2017) Fault-resilient localization for underwater sensor networks. Ad Hoc Networks 55:132–142. https://doi.org/10.1016/j.adhoc.2016.09.003
Davis A, Hwa Chang (2012) Underwater wireless sensor networks. In: 2012 Oceans. IEEE, pp 1–5
Ghasemi-Marzbali A (2020) A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm. Soft Comput 24:13003–13035. https://doi.org/10.1007/s00500-020-04721-1
Gkikopouli A, Nikolakopoulos G, Manesis S (2012) A survey on Underwater Wireless Sensor Networks and applications. 2012 20th Mediterr Conf Control Autom MED 2012 - Conf Proc 1147–1154. https://doi.org/10.1109/MED.2012.6265793
Gumaida BF, Luo J (2019) A hybrid particle swarm optimization with a variable neighborhood search for the localization enhancement in wireless sensor networks. Appl Intell 49:3539–3557. https://doi.org/10.1007/s10489-019-01467-8
Guo Y, Liu Y (2013) Localization for anchor-free underwater sensor networks. Comput Elect Eng 39(6):1812–1821. https://doi.org/10.1016/j.compeleceng.2013.02.001
Han D, Yu Y, Li K-C, de Mello RF (2020) Enhancing the sensor node localization algorithm based on improved DV-hop and DE algorithms in wireless sensor networks. Sensors 20:343. https://doi.org/10.3390/s20020343
Han G, Zhang C, Shu L et al (2013) A survey on deployment algorithms in underwater acoustic sensor networks. Int J Distrib Sens Networks 9:314049. https://doi.org/10.1155/2013/314049
Hao Y, Simoncini V (2021) The Sherman–Morrison–Woodbury formula for generalized linear matrix equations and applications. Numer Linear Algebr with Appl nla.2384. https://doi.org/10.1002/nla.2384
He S, Chen J, Li X et al (2014) Mobility and intruder prior information improving the barrier coverage of sparse sensor networks. IEEE Trans Mob Comput 13:1268–1282. https://doi.org/10.1109/TMC.2013.129
He T, Huang C, Blum BM, et al (2003) Range-free localization schemes for large scale sensor networks. In: Proceedings of the 9th annual international conference on Mobile computing and networking - MobiCom ’03. ACM Press, New York, New York, USA, p 81
Hu K, Song X, Sun Z et al (2018) Localization based on MAP and PSO for drifting-restricted underwater acoustic sensor networks. Sensors 19:71. https://doi.org/10.3390/s19010071
Ismail N-SN, Hussein LA, Ariffin SHS (2010) Analyzing the performance of acoustic channel in underwater wireless sensor network (UWSN). In: 2010 Fourth Asia international conference on mathematical/analytical modelling and computer simulation. IEEE, pp 550–555
Jia M, Gu X, Guo Q et al (2016) Broadband hybrid satellite-terrestrial communication systems based on cognitive radio toward 5G. IEEE Wirel Commun 23:96–106. https://doi.org/10.1109/MWC.2016.1500108WC
Jia M, Liu X, Gu X, Guo Q (2017a) Joint cooperative spectrum sensing and channel selection optimization for satellite communication systems based on cognitive radio. Int J Satell Commun Netw 35:139–150. https://doi.org/10.1002/sat.1169
Jia M, Yin Z, Guo Q et al (2017b) Waveform design of zero head DFT spread spectral efficient frequency division Multiplexing. IEEE Access 5:16944–16952. https://doi.org/10.1109/ACCESS.2017.2740267
Nian-qiang L, Ping L (2008) A range-free localization scheme in wireless sensor networks. In: 2008 IEEE International symposium on knowledge acquisition and modeling workshop. IEEE, pp 525–528
Li S (2011) TDOA Acoustic Localization
Li Z, Li R, Wei Y, Pei T (2010) Survey of localization techniques in wireless sensor networks. Inf Technol J 9:1754–1757. https://doi.org/10.3923/itj.2010.1754.1757
Luo J, Fan L, Wu S, Yan X (2018) Research on localization algorithms based on acoustic communication for underwater sensor networks. Sensors 18:67. https://doi.org/10.3390/s18010067
Meyer F, Hlinka O, Wymeersch H et al (2016) Distributed localization and tracking of mobile networks including noncooperative objects. IEEE Trans Signal Inf Process over Netw 2:57–71. https://doi.org/10.1109/TSIPN.2015.2511920
Mortazavi E, Javidan R, Dehghani MJ, Kavoosi V (2017) A robust method for underwater wireless sensor joint localization and synchronization. Ocean Eng 137:276–286. https://doi.org/10.1016/j.oceaneng.2017.04.006
Naruei I, Keynia F (2021) Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems. Eng Comput. https://doi.org/10.1007/s00366-021-01438-z
Nazir U, Shahid N, Arshad MA, Raza SH (2012) Classification of localization algorithms for wireless sensor network: a survey. In: 2012 International conference on open source systems and technologies. IEEE, pp 1–5
Niculescu D, Nath B (2003) DV based positioning in ad hoc networks. Telecommun Syst 22:267–280. https://doi.org/10.1023/A:1023403323460
Peng B, Li L (2015) An improved localization algorithm based on genetic algorithm in wireless sensor networks. Cognit Neurodyn 9:249–256. https://doi.org/10.1007/s11571-014-9324-y
Piovesan N, Erseghe T (2018) Cooperative LOCALIZATION in WSNs: a hybrid convex/nonconvex solution. IEEE Trans Signal Inf Process Over Netw 4:162–172. https://doi.org/10.1109/TSIPN.2016.2639442
Priyantha NB, Balakrishnan H, Demaine E, Teller S (2003) Poster abstract: Anchor-free distributed localization in sensor networks. SenSys’03 Proc First Int Conf Embed Networked Sens Syst 340–341
Qi Y, Cheng P, Bai J et al (2016) Energy-efficient target tracking by mobile sensors with limited sensing range. IEEE Trans Ind Electron 63:6949–6961. https://doi.org/10.1109/TIE.2016.2584000
Rao PCS, Jana PK, Banka H (2017) A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wirel Netw 23:2005–2020. https://doi.org/10.1007/s11276-016-1270-7
Safavi S, Khan UA (2018) Localization in mobile networks via virtual convex hulls. IEEE Trans Signal Inf Process over Netw 4:188–201. https://doi.org/10.1109/TSIPN.2017.2673238
Saha S, Arya RK (2021) Adaptive virtual anchor node based underwater localization using improved shortest path algorithm and particle swarm optimization (PSO) technique. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.6552
Savvides A, Park H, Srivastava MB (2002) The bits and flops of the n-hop multilateration primitive for node localization problems. In: Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications - WSNA ’02. ACM Press, New York, New York, USA, p 112
Shakila R, Paramasivan B (2021) An improved range based localization using Whale Optimization Algorithm in underwater wireless sensor network. J Ambient Intell Humaniz Comput 12:6479–6489. https://doi.org/10.1007/s12652-020-02263-w
Shakshuki E, Elkhail AA, Nemer I et al (2019) Comparative study on range free localization algorithms. Procedia Comput Sci 151:501–510. https://doi.org/10.1016/j.procs.2019.04.068
Shang Y, Rumi W, Zhang Y, Fromherz M (2004a) Localization from connectivity in sensor networks. IEEE Trans Parallel Distrib Syst 15:961–974. https://doi.org/10.1109/TPDS.2004.67
Srinivasa Rao PC, Banka H (2017) Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach. Wirel Netw 23:433–452. https://doi.org/10.1007/s11276-015-1156-0
Srinivasa Rao PC, Banka H, Jana PK (2016) A gravitational search algorithm for energy efficient multi-sink placement in wireless sensor networks. pp 222–234
Ullah I, Liu Y, Su X, Kim P (2019) Efficient and accurate target localization in underwater environment. IEEE Access 7:101415–101426. https://doi.org/10.1109/ACCESS.2019.2930735
Wang Z, Feng X, Qin H et al (2017) An AUV-aided routing protocol based on dynamic gateway nodes for underwater wireless sensor networks. J Internet Technol 18:333–343. https://doi.org/10.6138/JIT.2017.18.2.20161122
Williams DP (2012) AUV-enabled adaptive underwater surveying for optimal data collection. Intell Serv Robot 5:33–54. https://doi.org/10.1007/s11370-011-0102-y
Wilson H, Gross L (2019) Reflection-constrained 2D and 3D non-hyperbolic moveout analysis using particle swarm optimization. Geophys Prospect 67:550–571. https://doi.org/10.1111/1365-2478.12758
Yan J, Li X, Luo X, Guan X (2017) Virtual-lattice based intrusion detection algorithm over actuator-assisted underwater wireless sensor networks. Sensors 17:1168. https://doi.org/10.3390/s17051168
Yan J, Xu Z, Luo X et al (2019) Feedback-based target localization in underwater sensor networks: a multisensor fusion approach. IEEE Trans Signal Inf Process Over Netw 5:168–180. https://doi.org/10.1109/TSIPN.2018.2866335
Yang J, Cai Y, Tang D, Liu Z (2019) A novel centralized range-free static node localization algorithm with memetic algorithm and Lévy flight. Sensors 19:3242. https://doi.org/10.3390/s19143242
Shang Y, Ruml W (2004) Improved MDS-based localization. In: IEEE INFOCOM 2004. IEEE, pp 2640–2651
Zhang Y, Liang J, Jiang S, Chen W (2016) A localization method for underwater wireless sensor networks based on mobility prediction and particle swarm optimization algorithms. Sensors 16:212. https://doi.org/10.3390/s16020212
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This research was supported by the Ministry of Electronics and Information Technology, Govt. of India, Grant No. 13(29)/2020-CC&BT.
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Saha, S., Arya, R. Improved hybrid node localization using the wild horse optimization in the underwater environment. Int J Syst Assur Eng Manag 14 (Suppl 3), 865–885 (2023). https://doi.org/10.1007/s13198-021-01388-1
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DOI: https://doi.org/10.1007/s13198-021-01388-1